Personalization

No doubt you’ve heard a lot about artificial intelligence and how it’s changing the world of business. However, have you thought about the way it’s working in entertainment and media to bring more meaningful experiences for audiences?

Adding machine learning to entertainment companies can create a more targeted approach to providing what audiences want to see. Considering entertainment has to rely strictly on fickle demographics, this can change everything for consumers and media companies.

Take a look at five burning issues being solved by A.I., including an important category: Analytics.

1. The Use of A.I. in Creating Trailers
We all know the entertainment industry has to rely on sponsors to keep themselves profitable. While streaming has changed the game, the content put forward for subscribers is being more fine-tuned thanks to analytics A.I.

It’s also helping to create what you see in the way of trailers. Two years ago, IBM worked with 20th Century Fox on using machine learning to create a trailer for the horror film “Morgan.” This was the first time a movie trailer was edited using a machine learning process.

This worked by using IBM’s AI program to select specific moments from the film based on the most effective visual, audio, or scene composition elements.

The result was a trailer made exclusively from AI, and likely won’t be the last one.

While this went strictly on the effectiveness of film editing, what about machine learning providing more targeted analytics?

2. Ad-Based Analytics
AI-powered interfaces are already being created in major entertainment companies to help create more targeted ads to audiences. Ad reps enter data into the program based on client meetings to gain analytics on what ads would work the most effectively for specific demographics.

Perhaps this sounds like nothing new, but it’s still evolving. In some cases, this means machine learning going even further by scanning online channels to scope out viewer opinions. It frequently involves the program scanning social media to capture more detailed data.

3. More Personalized and Flexible Viewing Experiences
The real greatness of machine learning today is how it’s creating personalized entertainment experiences and more flexibility. Today’s audiences want numerous options in how they view media content. Human beings can’t possibly manage all of this effectively without the help of machine learning.

At the core of this is A.I. automation, bringing specific entertainment to certain devices or formats on demand. Machine learning platforms understanding human cognitive processes is still a complex procedure, and it’s still in evolution mode now.

4. Viewer Predictions on Places Like Netflix
Even Netflix saw the huge potential in machine learning. A couple of years ago, they helped develop an application called Meson that better manages their machine learning pipelines. These algorithms are how Netflix helps bring so many accurate streaming or DVD recommendations for their subscribers.

Recommendation engines are a major part of machine learning now in helping engage more personally with customers. As A.I. becomes smarter in sorting data, the more intuitive recommendations become to help users find what they want.

It might even look a little scary to some users, even if it’s really working to retain their visitations.

5. Futuristic Innovations Ahead
In development now is machine learning in hi-tech products like interactive children’s books with sound effects and even holograms.

Other A.I. tech includes sampled celebrity voices designed to read anything of your choosing. Even animating 2D characters for animation projects is already underway.

According to Variety above, theme parks like Disneyland have also begun to use machine learning to better accommodate guests.

More advertising fits into this picture, of course. Better targeted machine learning is becoming refined so those in entertainment can truly understand every whim of their audiences.

Visit our website to find out how we can bring A.I. technology to your entertainment and media brands.

Personalization is more than just putting your recipients’ first name in the subject line of an email address. Truly personalized customer experiences can set you apart from your competition, and help your business develop fruitful long-term relationships with your audience.

True customer personalization is not easy. But executed correctly, and built on tangible audience intelligence and research, it can help you build and improve brand relevancy, loyalty, and revenue.

The Essence of Customer Personalization

We’re long removed from mass markets in which brands simply produce a one-size-fits-all product and surround it with a compelling marketing message. Going in 2017, personalization is key to success.

Every member of your target audience has unique needs, wants, and preferences. They may fit into overarching themes, but still diverge in significant ways. A single stay-at-home person will look for very different features in a new vehicle than a business professional will need for their daily commute.

Naturally, only personalized product development and messaging can equally address both of these needs. Do it right, and you’ll build a loyal customer base through relevant messaging and product development that brings in consistent revenue.

Building Message and Product Relevancy

The more you know about your audience, the better you can personalize your strategy. Account-based marketing is built on the fact that each potential client needs and deserves a customized promotional and sales strategy optimized for their unique buyer’s journey. True customization embraces the same concept.

Brands are beginning to recognize the importance of placing potential customers at the center of your messaging strategy. Caveat emptor is no longer a popular phrase; in fact, in 2017, it’s a sure way to failure.

Personalization, on the other hand, can accomplish increased relevance for each of your customers. And the results, especially as they relate to creating a more relevant messaging and product strategy, are both tangible and significant.

Especially in e-commerce, but ultimately across industries, personalization is key. A 2015 survey of 500 online shoppers found that 3 out of 4 retail emails are irrelevant to consumers’ current needs and preferences. Simply blasting out a promotional email to your entire contact database, in other words, will not lead to success.

Improving Brand Loyalty

Not surprisingly, personalization after the sale can also make a difference in engaging your customers and increasing brand loyalty. In the end, driving repeat purchases depends on two variables: satisfaction with the product, and a perceived brand relationship. Effective personalization can play a significant part in influencing both.

A Bain survey of more than 1,000 online shoppers found that while less than 10% have tried customization options, 25% to 30% are interested in doing so. While it is hard to gauge the overall potential of customization, if 25% of online sales of footwear were customized, that would equate to a market of $2 billion per year.

It’s a natural effect. If you allow your audience to customize their product, they’ll be more likely to appreciate the result. Because they take ownership in the final product they purchase, they have a stake in seeing it succeed.

In addition, product customization allows your customers to build products specifically designed for their needs. No brand, especially on a larger scale, can reliably offer customized product options for every single potential customer. Customization at the point of sale, then, adds an extra element of personalization that would otherwise be impossible to achieve.

Personalization and Brand Relationship

Whereas product customization is at the core of customer satisfaction, message personalization is the driving force in building a sustainable relationship between brand and customers. The more effectively you can speak to individual customers, the more likely you will be to gain their appreciation and drive them toward a repeat sale.

Numerous studies have shown that personalized messaging results in an uptick in metrics that range from email to app installs. Again, it’s a common sense conclusion: the more relevant you can make your message, the more likely your audience is to respond to it.

After the sale, of course, the impact of that type of message personalization is magnified. Now, you have a large scale of information about each customer that you can use for your messaging relevancy. The type of product bought can result in follow-ups relevant to that product, while the personal information like a birthday can be used for customer-specific promotions.

The result, naturally, is a relationship between customer and brand that ultimately drives loyalty.

Driving Sustainable Revenue

Providing a more relevant customer experience drives loyalty. Repeat purchases, in turn, increase your sales significantly. In fact, 82% of companies agree that it’s cheaper to convince an existing customer to buy from you again than it is to acquire a new customer.

It’s not a stretch, then, to conclude that customer personalization can be a significant tool in helping you build and improve your revenue in a sustainable way.

A 2015 report by VentureBeat provides evidence for that point. A 2014 article by Adobe confirmed as much: if 9 of 10 consumers state that personalization has an impact on their purchasing decision, it’s no surprise that 8 of 10 brands embracing the concept have seen their revenue rise as a result.

Personalization matters, both for first-time customers and repeat purchases. Shared values build brand relationships, and it’s on the brand to both find out what values you share with each customer, and communicate these values in an effective, sustainable, personalized manner.

How to Build Personalization Through Actionable Analytics

Why should you personalize and customize your marketing and product strategy? Given the strategy’s impact on your messaging relevance, brand loyalty, and revenue, the answer is clear. That leaves one, final question: how can you build an effective customer personalization strategy?

The short answer: effective, actionable analytics. To be successful, personalization requires an effective method to not just collect data about your audience, but organize and prioritize that data in a way that can help you gain actual, valuable insights about your audience’s needs and desires.

It’s no surprise that brands who excel in customer personalization also embrace big data. At the same time, the second step – data analysis – is just as important. Without it, you risk ending up in a swamp full of irrelevant data. In that case, finding the data points that actually allow you to personalize your strategy and speak to your audience is akin to the proverbial needle in a haystack.

Find the insights that make your potential and current customers tick, and you have a starting point. Then, design a messaging strategy around these insights to ensure positive, sustainable customer personalization.

For example, you may find that in your industry, customers express very different needs and desires around a general ‘improve my life’ pain point. The more you can narrow down these needs, the better marketing strategy you can build to ensure that each of these needs is adequately addressed in personal communication with your audience.

Through modern analytics and marketing solutions, you can build this type of personalization on a mass scale while still ensuring enough human touches to encourage sustainable brand relationships. Customer personalization, at its heart, is just that: a relationship with your brand that, when successful, builds relevancy, loyalty, and revenue.

Artificial intelligence grows incredibly more sophisticated every year, and one of the most interesting developments is Neural Network Intelligence. If you thought AI has already turned a corner on mimicking human intelligence, Artificial Neural Networks (or ANN’s) might soon make this happen faster. The point is to imitate more rational thinking and deductive reasoning capabilities.

Up until recently, trying to replicate complex functions of a human brain wasn’t possible in AI programs. Things have started to advance quickly, and it’s time to learn about what this means in AI to enhance your business’s value.

In many cases, it can lead to more personalized experiences for your customers to help them and help you make smarter business decisions.

Before you get there, though, you need to learn about the technological and scientific aspects behind Neural Network Intelligence. It look more and more like a human, including learning through continued user interactions. In this case, it works similar to machine learning where it builds up superior intelligence over time.

Let’s look at how neural networks work and how you can apply this to bring more contextual personalization to customer experiences.

The Science Behind Artificial Neural Networks

When you delve into the science behind “ANNs”, the intention is to recreate brain connections using silicon and wires. Thanks to new advancements, AI recently transformed enough to build something resembling neurons and dendrites.

This occurs by creating multiple nodes, mimicking how neurons work. Just like neuron links, nodes have output as well, otherwise known as node values. Each of these connections have a weight, or an integer number controlling the signal. The weight in each connection can become adjusted based on the node output’s quality.

The topology behind this fall into two basic categories: FreeForward and FeedBack. For the former, it’s used strictly for pattern generation, recognition, or classification. In the latter, you’re creating feedback loops. In other words, it’s where you get into neural patterns to help you make better business decisions.

Since you’re bringing machine learning into this concept, you’ll frequently see neural networks use several different learning strategies. Supervised learning is more for pattern recognition, but unsupervised learning uses clusters to help find hidden patterns.

Reinforcement learning goes on observation, and that’s where Neural Networks truly shine to change how you create personalized experiences.

Processing Information in Real-Time

You’ve likely read a lot about real-time tools and how incredibly useful they are to make faster business decisions. AI now plays a major part in this thanks to Neural Networks. The latter uses human brain functions to learn through processing information in real-time so it becomes “smarter” with more user interaction.

This continues to improve and adjusts to any changes based on what a user prefers. For instance, if a user has specific preferences, the AI program is going to alter itself to suit a customer’s buying habits and whims. Any volatile behavior allows adjustments based on sudden changes in customer preferences.

What this does is bring recommendations on how you should approach communication with your customers. In metaphorical terms, it’s AI acting as an all-thinking oracle giving real-time results on how to personalize the marketing and buying experience.

In all, this replicates the feel of customers interacting with a well-trained sales associate. Instead, it’s done entirely online to give a customer the ultimate buying experience tailored just for them.

The problem is, many companies continue to use outdated forms of AI that don’t completely look at the customer as an individual.

AI Platforms Looking at Population and Probability

To show how fast AI changes, many businesses still use an older version of AI using recommendations via study of populations and probability. A couple of years ago, this was the best choice to personalize customer relationships. While better than no personalization at all, it still didn’t dig deep enough into analyzing individual buyers.

The focus was more on past behaviors as a whole, which was a good introduction for what AI could do for businesses. Also going by probability, it only gave a partial picture of what a customer might or might not do.

Having AI think like a human brain allows it to think more abstractly and fully understand consumer complexity. No one person is alike, and each customer is going to have their own pain points to integrate into your personal approaches.

Another weakness of older AI is it didn’t effectively accommodate new product lines in your business. Cutting this out of the recommendation schema created mass blind spots to product catalog performance. The only solution was to add it manually, and this led to downtime and lost revenue.

The Business Value Impact of Neural Networks in AI

You’ll find significant evidence showing personal one-on-one experiences are a vital part of today’s commerce structure. Regardless, many marketing analysts note that personalizing experiences can backfire if you don’t make it relevant to a customer’s life.

This is where Neural Network AI is going to help bring major business value by further understanding customer likes, dislikes, and intentions.

In the end, you’ll be able to increase more sales per customer, increase customer retention, create more loyalty, help your shopping cart conversions, and improve customer retention value.